Rolling average: how to smooth data for better trend analysis

Ever looked at a data graph and wondered, "What on earth is going on here?" You're not alone. Data can get pretty wild, especially when volatility throws curveballs into your trend analysis.

In this post, we'll dive into how data volatility can muddy the waters of interpreting trends, and how techniques like rolling averages can help smooth things out. Plus, we'll touch on how Statsig's features can make this process a whole lot easier.

The impact of data volatility on trend analysis

Dealing with data can sometimes feel like trying to spot patterns in a snowstorm—everything's changing so fast, it's hard to make sense of it all. When data is volatile, those short-term fluctuations can really mess with our ability to see the bigger picture. This makes interpreting economic and behavioral trends a tricky business.

But don't worry, there are ways to smooth things out. Techniques like rolling averages help cut through the noise, revealing the true direction of your data.

Ever watched a dataset jump from +15 one day to -10 the next? It's enough to give anyone whiplash! Such wild leaps make it tough to figure out what's really going on, especially when you're dealing with datasets over specific time periods. That's why smoothing out these ups and downs is so important—it helps us see the real movement behind all that noise.

If we don't smooth out the data, we might end up making decisions based on misleading averages. And nobody wants that! Martin Fowler points out that just looking at averages can hide important details in how data is spread out. So, to really get what's going on, we've gotta use methods that cut through the noise and bring the true signal to the surface.

That's where rolling averages come into play—they're super handy for smoothing out wild data swings. By averaging several data points over a set window, they help iron out the short-term bumps and bring out the underlying trend. This is especially useful when you're looking at things like economic indicators, sales figures, or how users behave on your app or website.

Now, picking the right window size for your rolling average is kinda like choosing the right zoom level on a map. Bigger window sizes make for smoother trend lines, but you might miss out on the latest twists and turns. Go too small, and while you'll catch the immediate changes, you might not see the long-term patterns. Striking the right balance is the trick to getting meaningful insights from all that volatile data.

Rolling averages: a fundamental smoothing technique

Rolling averages—also known as moving averages—are a go-to method for smoothing out volatile data. Basically, they work by calculating the average of data points over a moving window, which helps reduce those pesky short-term fluctuations and highlights the longer-term trends. So, whether you're analyzing economic data, stock prices, or any noisy time series, rolling averages can be a real lifesaver.

There are several flavors of rolling averages, each with its own twist. The most basic is the simple moving average, which treats all data points in the window equally. Then there's the weighted moving average, which gives more importance to recent data. And let's not forget the centered moving average, which takes into account both past and future data points.

Using rolling averages in tools like Statsig's Metric Drilldown is super flexible. You can tweak the window size to control how much smoothing you want. Bigger windows will smooth out the curves more, while smaller windows will make your chart more responsive to recent changes. This means you can tailor your analysis to exactly what you're interested in—be it long-term trends or the latest fluctuations.

Just keep in mind, though, that rolling averages—like any average—can sometimes hide what's really going on underneath. Martin Fowler points out that averages can obscure the true data distribution. So, while rolling averages are a powerful tool, it's a good idea to use them alongside other techniques, like data visualization. By combining them with histograms, box plots, or violin plots, you can uncover insights that might be buried in the noise.

Applying rolling averages to real-world datasets

Economists are all about rolling averages. They use them to make sense of volatile economic indicators, especially after seasonal adjustments. This helps them spot the underlying trends that might be hiding behind all those short-term ups and downs.

Data analysts love rolling averages too. They use them to smooth out datasets that have sudden jumps, making it easier to interpret trends. By grouping data points into longer time units, rolling averages give us a better view of where the data is really headed.

Rolling averages are especially handy when you're dealing with datasets that have irregular patterns. They help you spot significant shifts that might otherwise go unnoticed. For example, Martin Fowler suggests using data visualizations to highlight distributions, rather than just relying on averages, which can sometimes be misleading.

When it comes to product analytics, tools like Statsig's Metric Drilldown chart make rolling averages a breeze. They let you apply rolling averages to your data, helping to smooth out daily fluctuations and highlight those longer-term trends. This means you can analyze user metrics and behaviors more accurately. Statsig makes it easy to get the insights you need.

Thinking about becoming a data scientist? David Robinson recommends starting a blog to practice analyzing data and sharing your findings. Applying techniques like rolling averages to real-world datasets and sharing your work is a great way to show off your skills and build up your portfolio.

Leveraging rolling averages in advanced analytics tools

Advanced analytics tools—like Statsig's Metric Drilldown Charts—offer rolling averages to take your data analysis to the next level. With these tools, you can customize the rolling average period, adjusting the granularity to highlight the trends that matter most to you. By visualizing smoothed data, you'll be able to make better decisions based on clearer insights.

Rolling averages shine when you're dealing with volatile data. They help smooth out those short-term fluctuations and reveal the underlying trends. This is super valuable in business, where just relying on averages can sometimes hide important insights.

When you're using rolling averages in analytics tools, you can do all sorts of cool things:

• Adjust the rolling average period to zero in on specific time frames.

• Compare smoothed data across different segments or cohorts.

• Spot trends and anomalies more easily.

It's all about getting the most meaningful insights out of your data.

Say you're looking at user behavior—you might use a 7-day rolling average to catch weekly patterns. Or maybe you want to compare different marketing campaigns; a 30-day rolling average can help you see their long-term impact.

If you're an aspiring data scientist, why not start a blog to practice with real-world datasets? You could dive into topics like computing stock profits and losses or using empirical Bayes for baseball stats. Sharing your analyses helps you build skills and a professional portfolio.

Closing thoughts

Data volatility doesn't have to be a headache. By using techniques like rolling averages, you can smooth out the noise and uncover the real trends hiding in your data. Tools like Statsig make this process even easier, providing flexible options to customize your analysis.

If you're keen to learn more, check out the resources linked throughout this post. Whether you're an economist, data analyst, or aspiring data scientist, mastering rolling averages will enhance your data analysis toolkit.

Hope you found this helpful!